R Notebook

library(tidyverse)

Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ───────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✓ ggplot2 3.3.5     ✓ purrr   0.3.4
✓ tibble  3.1.3     ✓ dplyr   1.0.7
✓ tidyr   1.1.3     ✓ stringr 1.4.0
✓ readr   2.0.1     ✓ forcats 0.5.1
── Conflicts ──────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

library(janitor)

Attaching package: ‘janitor’

The following objects are masked from ‘package:stats’:

    chisq.test, fisher.test

library(readODS)
library(httr)
library(here)

here() starts at /Users/tomdavie/Documents/GitHub/ev_climate_change_project

library(data.table)

Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     
data.table 1.14.0 using 1 threads (see ?getDTthreads).  Latest news: r-datatable.com
**********
This installation of data.table has not detected OpenMP support. It should still work but in single-threaded mode.
This is a Mac. Please read https://mac.r-project.org/openmp/. Please engage with Apple and ask them for support. Check r-datatable.com for updates, and our Mac instructions here: https://github.com/Rdatatable/data.table/wiki/Installation. After several years of many reports of installation problems on Mac, it's time to gingerly point out that there have been no similar problems on Windows or Linux.
**********

Attaching package: ‘data.table’

The following objects are masked from ‘package:dplyr’:

    between, first, last

The following object is masked from ‘package:purrr’:

    transpose

library(sf)

Linking to GEOS 3.8.1, GDAL 3.2.1, PROJ 7.2.1

library(leaflet)

Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio

read_ods(here("raw_data/electric-vehicle-charging-device-statistics-june-2021.ods"))

Error in .parse_ods_file(file) : file does not exist

getwd()

APE <- read_ods("raw_data/APE_site_data_tables.ods", sheet = 2)

NO2 <- read_ods("raw_data/NO2_tables.ods")

registry <- GET("http://chargepoints.dft.gov.uk/api/retrieve/registry/format/csv/") %>% 
  content()

type <- GET("http://chargepoints.dft.gov.uk/api/retrieve/type/format/csv/") %>% 
  content()

read_csv("Documents/GitHub/ev_climate_change_project/raw_data/no2_by_grid_2019.csv")

air_pollution_cap <- GET("https://uk-air.defra.gov.uk/data/sos/service?service=AQD&request=GetCapabilities")

air_pollution_cap <- rbindlist(air_pollution$content, fill = TRUE)

air_pollution_cap <- unnest(air_pollution_cap)

air_pollution <- GET("https://uk-air.defra.gov.uk/data/sos/service?service=SOS&version=2.0.0&request=GetObserved&observedProperty=http://dd.eionet.europa.eu/vocabulary/aq/pollutant/8") %>% 
  content()

air_pollution$exceptions

air_pollution <- rbindlist(air_pollution, fill = TRUE) 

Error in rbindlist(air_pollution, fill = TRUE) : 
  Input is data.table but should be a plain list of items to be stacked

air_pollution <- unnest(air_pollution)

How many electric vehicles are on the road across the UK by LA?

# Reading in and skipping first 5 rows
uk_ev <- read_ods(here("raw_data/ev_by_la.ods"), sheet = 2, skip = 5)
# Making row 1 the variable name
names(uk_ev) <- uk_ev[1,] 
# Removing row 1 
uk_ev <- uk_ev[-1,] %>% 
  clean_names()

# loading in shape file 
uk_shape_file <- st_read(here("raw_data/Local_Authority_Districts__April_2019__UK_BFE_v2-shp/Local_Authority_Districts__April_2019__UK_BFE_v2.shp")) %>%
  clean_names() %>% 
  st_simplify(dTolerance = 1000) %>%
  st_transform("+proj=longlat +datum=WGS84") %>% 
  select(lad19cd, long, lat, geometry) 

Error in (function (cond)  : 
  error in evaluating the argument 'x' in selecting a method for function 'select': Cannot open "/Users/tomdavie/Documents/GitHub/ev_climate_change_project/raw_data/Local_Authority_Districts__April_2019__UK_BFE_v2-shp/Local_Authority_Districts__April_2019__UK_BFE_v2.shp"; The file doesn't seem to exist.

# Joining uk_ev + shape file 
uk_ev_map <- uk_ev %>% 
  left_join(uk_shape_file, by = c("ons_la_code_apr_2019" = "lad19cd")) %>% 
  drop_na() %>% 
  mutate(across(c(x2021_q1:x2011_q4), as.numeric)) %>% 
  st_as_sf()

    pal <- colorBin("Greens", domain = uk_ev_map$x2021_q1, bins = c(0, 500, 1000, 2500, 5000, 10000, 15000))
    
    uk_ev_map_labels <- sprintf(
      "<strong>%s</strong><br/>%g Electric Vehicles",
      uk_ev_map$region_local_authority_apr_2019_3, uk_ev_map$x2021_q1) %>% 
      lapply(htmltools::HTML)

# Geospatial of EV Vehicles in the UK 2021 Q1
uk_ev_map %>% 
  leaflet() %>% 
  setView(lng = -4.2026, lat = 55.8, zoom = 4.7, options = list()) %>%
  addProviderTiles(providers$CartoDB.Positron) %>% 
  addPolygons(fillColor = ~pal(x2021_q1),
    weight = 0.1,
    opacity = 0.9, 
    color = "black",
    fillOpacity = 0.8,
    highlightOptions = highlightOptions(color = "green", weight = 2,
                                        bringToFront = TRUE),
    label = uk_ev_map_labels,
    labelOptions = labelOptions(
      style = list("font-weight" = "normal", padding = "3px 8px"),
      textsize = "15px",
      direction = "auto")) %>% 
  addLegend(pal = pal, values = ~x2021_q1, opacity = 0.7, title = NULL,
            position = "bottomright")

How many electric vehicles are on the road across the UK by LA?

# Reading in and skipping first 5 rows
uk_ev_postcode <- read_ods(here("raw_data/ev_by_postcode.ods"), sheet = 2, skip = 6) %>% 
  rename("postcode" = PostcodeDistrict2)

Error: Can't rename columns that don't exist.
x Column `PostcodeDistrict2` doesn't exist.
Run `rlang::last_error()` to see where the error occurred.

# loading in shape file 
uk_shape_file_postcode <- st_read(here("raw_data/postcode_shape/EX_Sample.shp")) %>%
  clean_names() %>% 
  st_simplify(dTolerance = 1000) %>%
  st_transform("+proj=longlat +datum=WGS84") #%>% 
#  select(lad19cd, long, lat, geometry) 

# Joining uk_ev + shape file 
uk_ev_map_postcode <- uk_ev_postcode %>% 
  left_join(uk_shape_file_postcode, by = c("ons_la_code_apr_2019" = "lad19cd")) %>% 
  drop_na() %>% 
  mutate(across(c(x2021_q1:x2011_q4), as.numeric)) %>% 
  st_as_sf()

    pal <- colorBin("Greens", domain = uk_ev_map$x2021_q1, bins = c(0, 500, 1000, 2500, 5000, 10000, 15000))
    
    uk_ev_map_labels <- sprintf(
      "<strong>%s</strong><br/>%g Electric Vehicles",
      uk_ev_map$region_local_authority_apr_2019_3, uk_ev_map$x2021_q1) %>% 
      lapply(htmltools::HTML)

# Geospatial of EV Vehicles in the UK 2021 Q1
uk_ev_map %>% 
  leaflet() %>% 
  setView(lng = -4.2026, lat = 55.8, zoom = 4.7, options = list()) %>%
  addProviderTiles(providers$CartoDB.Positron) %>% 
  addPolygons(fillColor = ~pal(x2021_q1),
    weight = 0.1,
    opacity = 0.9, 
    color = "black",
    fillOpacity = 0.8,
    highlightOptions = highlightOptions(color = "green", weight = 2,
                                        bringToFront = TRUE),
    label = uk_ev_map_labels,
    labelOptions = labelOptions(
      style = list("font-weight" = "normal", padding = "3px 8px"),
      textsize = "15px",
      direction = "auto")) %>% 
  addLegend(pal = pal, values = ~x2021_q1, opacity = 0.7, title = NULL,
            position = "bottomright")

# Wrangling to create an EV count over time plot 
uk_ev_longer <- uk_ev %>%
  # Pivot longer to get year and count columns
  pivot_longer(cols = c(x2021_q1:x2011_q4), names_to = c("year"), values_to = "no_of_ev") %>% 
  # Filter so we only have UK as a whole data AND we only want final numbers of the year so Q4 
  filter(region_local_authority_apr_2019_3 == "United Kingdom" & str_detect(year, "q4")) %>% 
  # Simplify to just show year
  mutate(year = case_when(str_detect(year, "2021") ~ "2021",
         str_detect(year, "2020") ~ "2020",
         str_detect(year, "2019") ~ "2019",
         str_detect(year, "2018") ~ "2018",
         str_detect(year, "2017") ~ "2017",
         str_detect(year, "2016") ~ "2016",
         str_detect(year, "2015") ~ "2015",
         str_detect(year, "2014") ~ "2014",
         str_detect(year, "2013") ~ "2013",
         str_detect(year, "2012") ~ "2012",
         str_detect(year, "2011") ~ "2011"),
         year = as.numeric(year),
         no_of_ev = as.numeric(no_of_ev))

Row binding grid NO2 data

no2_2010 <- read_csv(here("raw_data/no2_by_grid_2010.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2011 <- read_csv(here("raw_data/no2_by_grid_2011.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2012 <- read_csv(here("raw_data/no2_by_grid_2012.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2013 <- read_csv(here("raw_data/no2_by_grid_2013.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2014 <- read_csv(here("raw_data/no2_by_grid_2014.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2015 <- read_csv(here("raw_data/no2_by_grid_2015.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2016 <- read_csv(here("raw_data/no2_by_grid_2016.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2017 <- read_csv(here("raw_data/no2_by_grid_2017.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2018 <- read_csv(here("raw_data/no2_by_grid_2018.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2019 <- read_csv(here("raw_data/no2_by_grid_2019.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))

# Create empty data frame
no2_all <- data_frame()

# For each year, bind rows to one dataset
for (i in 2010:2019) {
  df_name <- paste0("no2_", i)
  df_input <- as.name(df_name)
  
  df <- eval(df_input) %>% 
    mutate(year = i)
  
no2_all <- bind_rows(no2_all, df)
  }

# Remove missing NO2 grid values
no2_clean <- no2_all %>% 
  filter(no2 != "MISSING")

library(proj4)
proj4string <- "+proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +ellps=airy +datum=OSGB36 +units=m +no_defs"

no2_clean_row <- no2_clean %>% 
  rowid_to_column()

# Source data
xy <- no2_clean_row %>% 
  select(x, y, rowid)

# Transformed data
pj <- project(xy, proj4string, inverse=TRUE)
latlon <- data.frame(xy, lat=pj$y, lon=pj$x)
final <-  merge(no2_clean_row, latlon, by.x = "rowid", by.y = "rowid") %>%
  filter(year == 2019) %>% 
  select(lat, lon, no2) 

final %>% 
  head()

no2_annual_mean <- read_ods(here("raw_data/NO2_tables.ods"), sheet = 3, skip = 2) %>% 
  clean_names()

no2_annual_mean_all %>% 
  ggplot() +
  aes(x = year, y = annual_mean_no2concentration_mg_m3) + 
  geom_point() +
  geom_smooth(se = FALSE) +
  theme_minimal() +
  scale_y_continuous(breaks = seq(0, 70, 10), limits = c(0, 70)) +
  theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  labs(x = "\nYear\n", 
       y = "\nAnnual Mean NO2 Conc mg m3\n",
       title = "\nNO2 over time in the UK\n") 

`geom_smooth()` using method = 'loess' and formula 'y ~ x'

forecast_no2 <- no2_annual_mean_all %>% 
  dplyr::select(year, annual_mean_no2concentration_mg_m3) %>% 
  tsibble()

Error: Can't determine index and please specify argument `index`.
Run `rlang::last_error()` to see where the error occurred.

autoplot(forecast_no2)